lsbuitrago/wnut_test_model

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.1136
• Validation Loss: 0.2519
• Train Precision: 0.6410
• Train Recall: 0.4486
• Train F1: 0.5278
• Train Accuracy: 0.9491
• Epoch: 2

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.3233	0.3078	0.5333	0.1914	0.2817	0.9334	0
0.1534	0.2625	0.6388	0.4019	0.4934	0.9455	1
0.1136	0.2519	0.6410	0.4486	0.5278	0.9491	2

Framework versions

• Transformers 4.26.1
• TensorFlow 2.10.0
• Datasets 2.10.1
• Tokenizers 0.13.2